Tell us what you run and what sits unread. We will tell you which model reads it, and reply within two working days.
Trained on your domain. Running on hardware you own. No cloud, no data leaving your site.
Every organization sits on data it has never read. Policies no one has time to search. Cameras no one has time to watch. The data is there. It sits unused.
Manar reads it. We train a registered model on your specific conditions, install it on hardware you own, and let it compound sharper every day it operates. The intelligence never leaves your walls.
The public record on generic AI adoption is now measured: most pilots return nothing, abandonment is rising, and the obstacles organizations name most are cost, data privacy, and security. Not one of these is a property of the intelligence. All of them are properties of how it was adopted: generic, rented, and cloud bound. Manar runs adoption the other way, in five stages, ending in a system you own, run by your staff, that met a number before you accepted it.
The Adoption Path →Every sector on this list has been running data for years that nobody has read. Manar trains a model on your specific conditions and deploys it on hardware you own, inside your building.
A deployment does not rebuild your site around it. The model plugs into the systems you already run, on hardware sized to the job, and works with what they already produce.
The cameras, machines, records, and software already running at a site. A registered model works with the systems operating today. It does not replace them.
The model reads data where it already lives. No export, no transfer to a third party, no rebuild of the operation around our software.
Reasoning happens on site, on hardware sized to the model deployed there, owned by the client or specified by Manar. Decisions, alerts, and answers delivered where the work happens.
None of this requires data to leave the building. Integration extends what a model can read. It does not change where the intelligence runs or who owns it.
A partner often already has the infrastructure, the install base, or the software a client uses every day. Manar trains the model behind it. However a deployment starts, the client's data stays in their building.
Manar trains the model, ships it, and installs it inside the client's environment. We hold the relationship from the first conversation to ongoing support.
A systems integrator or distributor already sells and maintains the hardware a client runs. Manar trains the model for that hardware. They keep the client and the install. We keep the model.
A partner's existing software calls the model as a feature inside their own product. Their customers see the partner's interface, not ours. We supply and train the model behind it.
A deployment starts with one of the registered models. It gets trained on your data, installed on your hardware, and kept current as your operation changes.
The House of Wisdom took what existed and refined it into something that did not exist before. Hikma works the same way. We take open foundation models and teach them what they have never encountered: local conditions and the data of your specific domain.
The open frontier advances every month. We begin with the strongest available foundation for each domain. The starting point is public. What it becomes is not.
Each foundation learns the data of the domain and environment it will serve. Local conditions. Local patterns. The record no competitor can collect at any speed.
Refined intelligence deploys on hardware the organization owns, behind its own walls, offline if required. Sovereign from the moment it leaves Hikma. Proprietary from the first inference.
You do. The model and the hardware it runs on belong to you from the moment installation completes. Nothing is licensed back, and nothing depends on Manar continuing to operate for you to keep using what was built.
You check it yourself. At handover, the network connection is physically removed while the system is answering a live question, in front of your own staff, and it keeps answering. Full detail is on the security page.
Because what you experienced has now been measured across thousands of organizations, and the causes are known: the tools were generic, rented, and cloud bound. The Adoption Path reverses each one, and a deployment is accepted only when it meets a number agreed on your conditions before work begins. The full record is on the adoption page.